Name
Abstract | ||
|---|---|---|
This paper studies a new motion estimation method based on convolutional sparse coding. The motion estimation problem is formulated as the minimization of a cost function composed of a data fidelity term, a spatial smoothness constraint, and a regularization based on convolution sparse coding. We study the potential interest of using a convolutional dictionary instead of a standard dictionary using specific examples. Moreover, the proposed method is evaluated in terms of motion estimation accuracy and compared with state-of-the-art algorithms, showing its interest for cardiac motion estimation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8903163 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
Ultrasound imaging, cardiac motion estimation, Convolutional dictionary, sparse representation | Fidelity,Neural coding,Convolution,Computer science,Sparse approximation,Algorithm,Minification,Regularization (mathematics),Motion estimation,Smoothness | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nelson Diaz | 1 | 0 | 1.01 |
| Adrian Basarab | 2 | 148 | 25.03 |
| Jean-Yves Tourneret | 3 | 1154 | 104.46 |
| Henry Arguello Fuentes | 4 | 2 | 2.42 |